ArticleBMC gastroenterology2026
A novel serum metabolite classifier for identifying Metabolic Dysfunction-Associated Steatotic Liver Disease (MASLD) integrating metabolomics and machine learning.
Article in BMC gastroenterology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
12 authors.
Funding
Abstract
backgroundMetabolic dysfunction-associated steatotic liver disease (MASLD) presents a growing global health burden, while reliable non-invasive biomarkers for identifying affected individuals remain limited. This study aimed to characterize serum metabolomic signatures associated with steatosis and fibrosis and to develop a clinically applicable metabolite-based classifier.
methodsUntargeted serum metabolomics was performed in a FibroScan-characterized discovery cohort (n = 35) to identify candidate metabolic features associated with hepatic steatosis and fibrosis burden. To provide biological context for the observed metabolic alterations, 16 S rDNA sequencing was subsequently conducted in paired fecal samples from a subset of participants (n = 27). Differential metabolites were then subjected to LASSO regression for feature selection and used to construct a random forest diagnostic model in a validation cohort comprising healthy controls (n = 19) and ultrasound-confirmed MASLD patients (n = 52).
resultsMetabolomic profiling revealed distinct metabolic patterns across different degrees of steatosis and fibrosis. A total of 55 and 46 metabolites were identified as differentially abundant in relation to steatosis and fibrosis burden, respectively. Microbiome analysis indicated alterations in gut microbial composition, and integrative correlation analysis suggested several potential microbe-metabolite associations, including two metabolite-genus pairs showing relatively strong correlations. LASSO regression selected a panel of ten metabolites as the most informative diagnostic features. Using these metabolites as input variables, a random forest classifier was constructed and achieved an area under the receiver operating characteristic curve (AUROC) of 0.87. Incorporation of four clinical variables (BMI, ALT, triglycerides, and HDL cholesterol) further improved model performance, yielding an AUROC of 0.94.
conclusionThis study characterizes systemic metabolic alterations associated with MASLD and presents a non-invasive diagnostic model integrating serum metabolites with clinical indicators. Together, these findings highlight the potential utility of metabolomics-assisted approaches for identifying MASLD and for improving biological understanding of disease-associated metabolic changes.
Indexed as
Identifiers
What Socratic holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.